Multi Modal DeepFake Detection Using CNN

The proliferation of deepfake content has raised serious concerns about the authenticity of digital media, with implications spanning from social misinformation to cybersecurity breaches. In this study, we propose a multi-model deepfake detection framework that integrates multiple convolutional neural networks (CNNs) to improve classification accuracy and robustness. Each model within the ensemble is trained to detect unique facial distortions and inconsistencies introduced by deepfake generation techniques. By leveraging the strengths of diverse architectures, the system effectively identifies manipulated media across varying formats and qualities. Experimental results on publicly available datasets demonstrate that the proposed multi-model approach outperforms single-model baselines in both precision and generalization. This work contributes to the growing field of AI-based media forensics by offering a scalable and effective solution to combat the evolving challenge of deepfakes.